Quivr vs Voyage AI

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Quivr RAG	Voyage AI RAG
Tagline	Open-source RAG framework for building custom AI assistants over your own documents in a few lines of Python.	State-of-the-art embedding models and rerankers purpose-built for retrieval and RAG.
Category	RAG	RAG
Pricing	Free· Open source (pip install quivr-core); pay only for LLM/vector-store usage	Freemium· Free tier: 200M free text tokens per account for current models (50M for older specialized). Text embeddings $0.00002–$0.00018 per 1K tokens depending on model tier. Rerankers $0.00002–$0.00005 per 1K tokens after 200M free. Multimodal $0.12 per 1M text tokens + $0.60 per 1B pixels. Batch API 33% discount. File storage $0.05/GB/month.
Model	Multi-model (OpenAI, Anthropic, Mistral, Gemma)	in-house (voyage-3.5, voyage-4 series, voyage-code-3, voyage-finance-2, voyage-law-2, voyage-multimodal-3.5, voyage-context-3, rerank-2.5)
Editorial score	8.4 / 10	—
Use cases	document-qacustom-knowledge-baserag-pipelineinternal-assistantschat-with-pdf	Production RAG chatbot over proprietary docsTwo-stage retrieval with embed + rerankCode search across a monorepoLegal contract semantic searchFinancial filings and research retrievalMultimodal image-and-text searchLong-context document embedding (32K tokens)Context-aware chunk embedding for dense passagesBatch embedding of large historical corporaMongoDB Atlas Vector Search backends
Pros	Genuinely open source and pip-installable, no vendor lock-in Model-agnostic: OpenAI, Anthropic, Mistral, and Gemma supported Minimal boilerplate to get a working RAG assistant running Pairs with Megaparse for tougher PDF and document ingestion Customizable pipeline with tools and web search when you need more	Consistently near the top of MTEB and BEIR retrieval leaderboards — measurable recall gains over OpenAI text-embedding-3-large in most public evaluations. Short output dimensions (as low as 256 or 512) cut vector storage and ANN latency 3x–8x versus 1536/3072-dim competitors. Domain-tuned models (code, finance, legal) meaningfully outperform general embeddings on in-domain corpora. voyage-context-3 embeds chunks with awareness of surrounding document context, reducing the classic 'lost context' problem in fixed-window chunking. Rerank-2.5 with instruction-following gives a clean two-stage retrieval pipeline without training a custom cross-encoder. Generous 200M-token free tier per account makes prototyping and small production workloads essentially free. Batch API offers a 33% discount for large offline embedding jobs. MongoDB acquisition (2025) means tight, ongoing integration with Atlas Vector Search.
Cons	Python library, not a hosted product or UI You manage infra, vector store, and evals yourself Documentation site is sparse compared to larger RAG frameworks LLM and embedding costs are on you	API-only closed models — no self-hosting option, so latency-sensitive or air-gapped deployments are ruled out. Not an end-to-end RAG stack — you still need a vector database, LLM, and orchestration layer, which increases integration surface. Post-MongoDB acquisition, product roadmap and standalone longevity depend on MongoDB's priorities. Domain models cover finance, legal, and code but nothing else — medical, scientific, or multilingual-heavy corpora fall back to general models. Documentation is competent but sparser than OpenAI's or Cohere's — fewer end-to-end recipes for advanced patterns like hybrid search or query expansion. Pricing per token is competitive but not the cheapest — self-hosted open models (e.g. BGE, E5) are free at inference if you have GPUs.
Website	core.quivr.com	www.voyageai.com

Pick Quivr if

✅ Genuinely open source and pip-installable, no vendor lock-in
✅ Model-agnostic: OpenAI, Anthropic, Mistral, and Gemma supported
✅ Minimal boilerplate to get a working RAG assistant running
✅ Pairs with Megaparse for tougher PDF and document ingestion

Pick Voyage AI if

✅ Consistently near the top of MTEB and BEIR retrieval leaderboards — measurable recall gains over OpenAI text-embedding-3-large in most public evaluations.
✅ Short output dimensions (as low as 256 or 512) cut vector storage and ANN latency 3x–8x versus 1536/3072-dim competitors.
✅ Domain-tuned models (code, finance, legal) meaningfully outperform general embeddings on in-domain corpora.
✅ voyage-context-3 embeds chunks with awareness of surrounding document context, reducing the classic 'lost context' problem in fixed-window chunking.

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